Understanding the Pattern of Failure

The insightful pattern discussed in Atul Gawande’s “Better” emphasizes a profound truth: recognizing and reflecting on our failures is crucial for personal and professional growth. This notion is encapsulated in the phrase “Learn How You Fail,” which posits that true ingenuity stems not from sheer intellect but from character—specifically, the courage to face our shortcomings and adapt.

Personal Reaction

I found this pattern both challenging and refreshing. Initially, it’s discomforting to focus on failures rather than celebrate successes. However, Gawande’s approach—focusing on identifying and understanding the causes of our failures—provides a constructive framework for turning apparent setbacks into stepping stones.

This perspective is not just about acknowledging weaknesses but actively choosing which battles are worth fighting. The notion that not all failures need to be addressed equally, and some might even be embraced as limitations, is liberating. It helps in setting realistic goals and focusing efforts where they can truly make a difference.

Changes in Professional Perspective

As someone aspiring to thrive in a fast-evolving sector, this pattern has shifted my thinking towards a more adaptive and resilience-oriented professional mindset. The emphasis on continuous self-assessment and setting realistic boundaries resonates deeply. It underlines the importance of focusing on areas where I can excel and accepting areas where I may never be the best. This realization takes away the useless pursuit of perfection in every domain, instead encouraging a strategic approach to skill development and goal setting.

Points of Disagreement

While I appreciate the core message of the pattern, I hold reservations about the practicality of some suggested exercises, such as writing and debugging code without initial testing. While this can reveal unforeseen errors and personal blind spots, it may not be the most efficient learning method for everyone. This approach could lead to frustration or demotivation, particularly for novices who might benefit more from immediate feedback and iterative learning.

Conclusion

“Learn How You Fail” is not just about understanding how to handle failure—it’s about strategically leveraging these insights to refine our skills, set achievable goals, and foster a mindset that views challenges as opportunities for growth. This pattern has encouraged me to embrace my limitations, prioritize my efforts, and continue pushing the boundaries of my capabilities with a clear and realistic vision.

From the blog CS@Worcester – Site Title by rkaranja1002 and used with permission of the author. All other rights reserved by the author.

Blog Entry:

As a student deeply involved in computer science, understanding the significance and methodologies of code review is pivotal. This week, I chose to delve into an article from freeCodeCamp, titled “Code Review: The Ultimate Guide,” which explores the intricacies of code reviews in software development. This resource is particularly relevant to our ongoing discussions in class about software quality and maintenance.

Summary of the Article:

The article comprehensively outlines what code review entails and why it is a critical practice in software development. It discusses the benefits, such as catching bugs early, improving code quality, and fostering team knowledge sharing. Moreover, it provides practical tips on how to conduct effective code reviews, emphasizing the importance of a constructive attitude and specific, actionable feedback.

Reason for Selection:

I selected this resource because, as we learn to code and develop software, understanding the peer review process is essential for professional growth and skill enhancement. This article not only complements our coursework but also offers practical advice that can be immediately applied in any coding environment.

Personal Reflection:

Reading about the detailed processes and benefits of code reviews has been enlightening. I learned that effective code reviewing goes beyond merely finding errors; it is about collaboration, learning, and improving as a team. This has changed my perspective on coding assignments and projects. Instead of viewing them as solitary tasks, I now see them as part of a broader, collaborative process.

I am particularly struck by the emphasis on the mindset and communication skills needed during code reviews. The idea that feedback should be constructive and focused on the code rather than the coder is something I plan to carry forward into my professional life. This approach not only minimizes potential conflicts but also enhances the learning environment, making it more open and conducive to improvement.

Application in Future Practice:

Going forward, I expect to apply the principles from this article in my class projects and eventually in my professional work. Understanding the dynamics of effective communication and feedback within code reviews will be crucial as I work with others on software development projects. This will help in creating more robust, error-free software and in building a supportive team environment.

It is an important skill for everyone regardless of their place in the chain of development. The principles also apply outside of the field of computer science as a successful review is a part of any team process.

From the blog CS@Worcester – Abe's Programming Blog by Abraham Passmore and used with permission of the author. All other rights reserved by the author.

What is Software Testing?

Software testing is the process of evaluating whether a program or application functions as intended. It serves as a form of quality control, aimed at identifying and rectifying any discrepancies or errors before production use. For instance, in the scenario of a budget-tracking app, testers would verify the accuracy of data input, functionality of buttons, and ease of navigation, among other aspects, to ensure a seamless user experience.

Levels of Testing

Testing levels, also referred to as levels of testing, encompass distinct phases or stages of assessing software throughout its development cycle. These levels target specific aspects of functionality, contributing to enhanced quality assurance and reduced defects. The primary testing levels include:

1. Unit Testing: This level involves testing individual components, such as methods and functions, in isolation to ensure their correctness and functionality. Automated unit testing is often recommended, allowing for efficient evaluation of code behavior. Unit testing is crucial for identifying bugs at an early stage of development, as it allows developers to test each component independently. By writing automated tests that cover various scenarios, developers can ensure that their code performs as expected.
2. Integration Testing: Integration testing assesses the interaction and integration between various units or modules within the software. It aims to detect any issues arising from coding errors or integration conflicts, facilitating seamless collaboration between components.During integration testing, developers test how different modules work together to ensure that they function properly as a whole. This ensures that any errors or inconsistencies between modules are detected and addressed before moving to the next stage of development.
3. System Testing: System testing evaluates the integrated system as a whole, verifying its compliance with specified business requirements. Automation tools can be leveraged to streamline system testing processes, ensuring comprehensive assessment of functional and non-functional aspects.System testing focuses on evaluating the entire software system against the defined requirements and specifications. It includes testing functionalities, performance, security, and compatibility to ensure that the software meets the desired standards.
4. Acceptance Testing: Acceptance testing focuses on validating the system’s functionality and performance against user-defined criteria. It encompasses aspects such as usability, security, compatibility, and reliability, either through manual evaluation or automation tools.Acceptance testing involves end-users or stakeholders testing the software to ensure that it meets their expectations and requirements. This stage ensures that the software is ready for deployment and use in a real-world environment.

Software Testing Sequence

A structured testing sequence is essential for thorough evaluation of software functionality. This sequence comprises four main stages:

1. Unit Testing: Testing individual components to ensure functionality.
2. Integration Testing: Evaluating interactions between integrated units.
3. System Testing: Assessing the integrated system as a whole.
4. Acceptance Testing: Validating the system against user-defined criteria.

Conclusion

Software testing is a critical process in the software development lifecycle. It serves as a safeguard against defects and ensures that software meets user expectations and business requirements. Skipping or neglecting testing can have detrimental consequences, undermining the utility and effectiveness of the software.

From the blog CS@Worcester – CS: Start to Finish by mrjfatal and used with permission of the author. All other rights reserved by the author.

Summary of the Pattern:

The “White Belt” pattern emphasizes the importance of approaching new learning experiences with a beginner’s mind, regardless of your level of expertise. It suggests setting aside previous knowledge and preconceptions to fully absorb new information. Drawing analogies from various fields like family therapy and programming, the pattern illustrates the benefits of adopting a mindset of not knowing. By embracing ignorance and being open to making mistakes, individuals can accelerate their learning and gain deeper insights.

Reaction:

As someone who takes pride in my skills, it’s easy to fall into the trap of believing I already know the best way to approach a problem. However, the “White Belt” pattern reminds me that there’s always more to learn and different perspectives to consider.

What to Do:

The pattern suggests practical steps to implement its principles. For example, it recommends trying to implement a program in a language with a different paradigm, or learning from someone in a different technological community to understand their perspectives. These actions challenge one’s comfort zone and encourage embracing new ways of thinking.

What I Found Thought-Provoking:

The analogy of wearing a white belt, signifying a beginner’s level, is powerful. It reminds me that true mastery comes from a willingness to continuously learn and adapt. Additionally, the idea of consciously unlearning and relearning is intriguing. It requires humility and courage but promises significant growth.

Changes in Thinking:

This pattern has reminded me in the importance of continuous learning and humility in any area of life. I aim to approach new challenges with a more open mind, ready to embrace the discomfort of being a beginner again. Instead of seeing setbacks as failures, I now view them as opportunities for growth.

Disagreements:

While I don’t disagree with the core principles of the pattern, I recognize that fully embracing a beginner’s mindset can be challenging, especially in environments that value expertise and efficiency. However, I do see a view of balance on where it can be useful to listen to new ideas/appreaches while incoperating past ideas. As I do not think the White Belt pattern suggest to blondly accept any and all new information without any rebuttals or input.

In conclusion, the “White Belt” pattern serves as a powerful reminder to approach learning with humility and openness. By unlearning and relearning, we can break through plateaus and continue our journey toward mastery.

From the blog CS@Worcester – CS: Start to Finish by mrjfatal and used with permission of the author. All other rights reserved by the author.

For this week’s blog, I decided to research a bit on performance testing because it is listed as a topic for a class. While doing research, I found a blog called “How To Do Performance Testing: Tips And Best Practices” by Volodymyr Klymenko. In this blog, he discusses the primary purpose of this kind of software testing, types of performance testing, its role in software development, common problems revealed by performance testing, tools, steps, best practices, and common mistakes to avoid. The blog was well organized and clearly simplified each topic.

The purpose of performance testing is to find potential performance bottle necks to identify possible errors and failures. This is to ensure that the software meets the performance requirements before it is released. Some types are load testing(response under many simultaneous requests), stress testing ( response under extreme load conditions or resource constraints), spike testing (response under a significant and rapid increase in workload that exceeds normal expectations), soak testing(simulates a gradual increase in end users over time to test the system’s long-term stability), scalability testing(how the system scales when the volume of data or users increases), capacity testing (which tests the traffic load based on the number of users but differs in scope) and volume testing(which tests the response to processing a large amount of data).

The next section discusses the role of performance testing. I found some of the bullet points to be a bit obvious like “improving the system’s overall functioning”, “monitoring stability and performance”, and “assessing system scalability”. The other points mentioned were “failure recovery testing”, ”architectural impact assessment”, “resource usage assessment” and “code monitoring and profiling”.

After discussing the role of performance testing, the author discussed the common issues revealed by performance testing. The problems discussed were speed problems, poor scalability, software configuration problems, and insufficient hardware resources. This section is broken down into many bullet points to explain each issue. I found it to be very beneficial in simplifying the ideas presented.

The last three sections are the steps for performance testing, best practices, and common mistakes to avoid. The steps presented in this article for performance testing were: 1.Defining the Test Environment, 2.Determination of Performance Indicators , 3. Planning and Designing Performance Tests , 4. Setting up the Test Environment, 5. Development of Test Scenarios, 6. Conducting Performance Tests , and 7. Analyze, Report and Retest. The section after explaining the steps includes some best practices like “Early Testing And Regular Inspections “ and “Testing Of Individual Blocks Or Modules”. Some of the common mistakes to avoid that I found to be noteworthy were “Absence of Quality Control System” and “Lack of a Troubleshooting Plan”. While working on the Hack.Diversity Tech.Dive project, I had to do some performance testing and even used a tool that was mentioned in the article called Postman. In the future, I will definitely be more prepared to do performance testing now that I can reference this article.

From the blog CS@Worcester – Live Laugh Code by Shamarah Ramirez and used with permission of the author. All other rights reserved by the author.

For this week’s Software Testing blog post, I wanted to have a glance into the world of security testing.

In a big picture sense, security testing is a simple concept to understand even from a layman perspective. It entails testing software for any vulnerabilities, risks or threats that can be posed and to any stakeholders in the software that could cause a negative impact. Stakeholders can include the company that creates and / or deploys the software, the end-user, and even the software itself.

There are many different types and methods to security testing, and in the blog post I would like to go through a couple that caught my mind, with information courtesy of Oliver Maradov’s very thorough security testing blog post on BrightSec.

Before getting into the actual methods, I found some key principles at the start of the article noteworthy to mention as well. Confidentiality and authorization have to do with limiting access to sensitive data, with authorization only allowing access on the basis of permission. Authentication is the principle of verifying identities that access data. Integrity and availability are crucial to preserving the consistency of data and making sure that is accessible when needed without failure. Lastly, non-repudiation sets a principle of of logging while further making sure data is accessible.

Now, for the first subject in the article that I found interesting. What first caught my eye was the use of the different ‘box’ testing methodologies, that is, black box, white box and grey box testing. The reason I was interested is because the application of black box testing makes a lot of sense (compared to the standard software development application, in my opinion) in a security context. Essentially, where black box testing for software developers mostly deals with the specifications prior to writing the code for the system, black box testing in a security context means approaching software that has been written through the prespective of an attacker. We typically see this through ethical hacking and penetration testing. I just found this a lot more compelling than the software engineer side of black box testing.

The second subject I found compelling was the section on DevSecOps. The idea of DevSecOps is to, as the name implies, merge the software development, security and operations process together to ensure the whole endeavor of creating a great piece of software is built from the ground up with good principles in a multi-faceted way, and to ensure that each team member has a level of understanding of the key principles. As such, the end product should (hopefully) become a product of a strong approach to each process.

From the blog CS@Worcester – V's CompSCi Blog by V and used with permission of the author. All other rights reserved by the author.

Based on this article by Denis Kranjcec: How I failed at Test-Driven Development and what it took to get it right

For the past couple weeks, Test-Driven Development has been the focus of our learning. We’ve done a small project in class creating a simple rover instruction program, which built our confidence and knowledge on how TDD works. Then, we used what we learned to individually create a unique word counter with string input. Since then, I have taken a slight liking to TDD, although it may just be a honeymoon phase. Well, my last blog post was about why Test Driven development is so good. This time, I sought out to find peoples’ personal failures with TDD, and why it failed. This is how I found How I failed at Test-Driven Development and what it took to get it right by Denis Kranjcec. 

Kranjcec begins his article by summarizing that he took a shot with TDD, and missed. But why did he miss? He dives into the reasons, and after a quick discussion on what TDD is and how it is supposed to improve code, he came to the conclusion that it wasn’t TDD’s fault, it was his. 

To start off, Kranjcec was using DDD (Domain-Driven Design), which led to his code being easy to comprehend, but his tests were more complex and difficult to maintain. After trying TDD again, he eventually realized that his biggest mistake was “starting with the simplest use cases and building up to the more complex ones” (Kranjcec). This seems to be a common problem with TDD users, as they tend to stick to more complicated tests than easier ones in exchange for fewer tests total. 

His solution was to change the way he wrote code and designed. This was done by breaking his work down into many smaller steps and lots of commits each day, in addition to refactoring his code. In short, he noticed his code was much simpler to understand and shorter, with other benefits as well.

I selected this article because after only seeing the benefits of TDD, I wanted to see the struggles people had with it. Although this article wasn’t about the faults of TDD, it shows the personal mishaps or mistakes that one can make when working with TDD, which can lead to a greater understanding of it in the end. 

I did like this article because it was an alternate perspective on how TDD can go. I learned to be patient with my code, and to try and stick with simpler tests and design in order to make TDD more effective. I think this article is a good reminder for self reflection on your own work, as it may not always be another’s fault if it doesn’t work. I hope to apply such knowledge in future projects using TDD, keeping in mind that simplicity is key.

From the blog CS@Worcester – Josh's Coding Journey by joshuafife and used with permission of the author. All other rights reserved by the author.